Vignete 4.- CampR con plotly y ggplot2
Fran Velasco
2024-05-21
Crear mapas de CampR con ggplot2.
La función ggplot2::map_data permite dar formato a Porc.map para pintarlo con ggplot2
Porc<-ggplot2::map_data(Porc.map)
head(Porc)hake<-CampR::maphist(1,50,"P16","Porc",out.dat=T,plot=F)## lan lat long prof peso.gr numero camp peso
## 1 1 51.1471 -13.9298 531.5 12053.973 9.0 2016 12.053973
## 2 2 51.1011 -14.1830 628.5 3898.051 3.0 2016 3.898051
## 3 3 51.1372 -14.4533 638.5 12443.778 9.0 2016 12.443778
## 5 5 51.3002 -14.5562 593.5 4857.571 3.0 2016 4.857571
## 6 6 51.4554 -14.7745 639.0 11844.078 7.5 2016 11.844078
## 7 7 51.6273 -14.7835 572.0 44137.931 30.0 2016 44.137931
## 8 8 51.7889 -14.7824 485.5 12983.508 10.5 2016 12.983508
## 9 9 51.9642 -14.8531 495.5 13403.298 10.5 2016 13.403298
## 10 10 52.1994 -14.6672 406.0 50104.948 34.5 2016 50.104948
## 12 12 52.5173 -14.6387 412.0 49295.352 33.0 2016 49.295352
## 13 13 52.4468 -14.3837 347.0 40149.925 28.5 2016 40.149925
## 14 14 52.4544 -14.1025 311.5 31035.982 27.0 2016 31.035982
## 15 15 52.3609 -13.8595 349.0 28302.849 25.5 2016 28.302849
## 16 16 52.4373 -13.5133 361.5 44647.676 36.0 2016 44.647676
## 17 17 52.2815 -14.4153 352.0 35982.009 36.0 2016 35.982009
## 18 18 52.1550 -14.1128 344.0 108125.937 93.0 2016 108.125937
## 19 19 52.1276 -13.8451 403.0 80539.730 49.5 2016 80.539730
## 20 20 52.2124 -13.4927 436.5 97001.499 75.0 2016 97.001499
## 21 21 51.8779 -14.4495 360.0 39340.330 22.5 2016 39.340330
## 22 22 51.7396 -14.4448 384.0 67151.424 45.0 2016 67.151424
## 23 23 51.5521 -14.5670 507.0 53583.208 36.0 2016 53.583208
## 24 24 51.4685 -14.1850 446.0 61109.445 43.5 2016 61.109445
## 25 25 51.6924 -13.7936 502.5 16881.559 12.0 2016 16.881559
## 26 26 51.7289 -14.1079 381.0 44407.796 27.0 2016 44.407796
## 27 27 51.8743 -13.9758 379.5 36581.709 21.0 2016 36.581709
## 28 28 51.9700 -13.7536 442.0 34992.504 28.5 2016 34.992504
## 29 29 52.8863 -12.8234 428.0 83268.366 57.0 2016 83.268366
## 30 30 52.8840 -12.6118 417.0 43319.340 42.0 2016 43.319340
## 31 31 52.9644 -12.3519 326.0 92563.718 175.4 2016 92.563718
## 32 32 53.0122 -12.0771 233.0 112773.613 454.3 2016 112.773613
## 33 33 53.5221 -11.6332 215.0 20569.715 61.5 2016 20.569715
## 34 34 53.8113 -11.4031 252.0 48062.969 52.5 2016 48.062969
## 35 35 53.9852 -11.3952 287.0 174497.751 121.4 2016 174.497751
## 36 36 53.9398 -11.9261 370.5 75772.114 70.5 2016 75.772114
## 37 37 53.8415 -13.3700 362.0 30014.993 22.5 2016 30.014993
## 38 38 53.9635 -12.9041 596.0 4917.541 3.0 2016 4.917541
## 39 39 53.8543 -12.5745 380.5 20059.970 22.5 2016 20.059970
## 40 40 53.8930 -12.1672 375.5 78860.570 70.5 2016 78.860570
## 41 41 53.4660 -12.6767 297.5 54158.921 54.0 2016 54.158921
## 42 42 53.5125 -12.4703 315.0 118665.667 96.0 2016 118.665667
## 43 43 53.5162 -12.2190 329.5 37952.024 39.0 2016 37.952024
## 44 44 53.6031 -11.9335 296.0 74962.519 63.0 2016 74.962519
## 45 45 53.3864 -12.0448 280.5 46248.876 185.9 2016 46.248876
## 46 46 53.3008 -12.4400 360.5 94146.927 134.9 2016 94.146927
## 47 47 53.3623 -13.0208 271.5 27496.252 43.5 2016 27.496252
## 48 48 53.1860 -12.7345 353.5 117503.748 122.9 2016 117.503748
## 49 49 53.0288 -12.8775 379.0 83169.415 103.4 2016 83.169415
## 50 50 52.9543 -13.0919 361.5 204197.901 242.9 2016 204.197901
## 51 51 53.6493 -13.6562 270.0 5922.039 9.0 2016 5.922039
## 52 52 53.5385 -14.1104 330.0 25187.406 27.0 2016 25.187406
## 53 53 53.3838 -14.1155 225.5 7976.012 21.0 2016 7.976012
## 54 54 53.3056 -14.3913 350.5 68230.885 63.0 2016 68.230885
## 55 55 53.1272 -14.3615 232.5 111754.123 97.5 2016 111.754123
## 56 56 53.0369 -14.7581 663.5 2371.814 1.5 2016 2.371814
## 57 57 52.7801 -14.4977 401.5 157061.469 128.9 2016 157.061469
## 58 58 52.7032 -14.8204 643.5 6836.582 4.5 2016 6.836582
## 59 59 52.6840 -14.1928 324.5 38530.735 27.0 2016 38.530735
## 60 60 52.9205 -14.0399 205.5 11229.385 97.5 2016 11.229385
## 61 61 53.0984 -13.3226 237.5 13493.253 27.0 2016 13.493253
## 62 62 52.9616 -13.5822 209.5 6674.663 16.5 2016 6.674663
## 63 63 52.8107 -13.7734 212.0 10344.828 33.0 2016 10.344828
## 64 64 52.8229 -13.3382 288.0 105142.429 107.9 2016 105.142429
## 65 65 52.5408 -13.7273 313.0 18590.705 18.0 2016 18.590705
## 66 66 52.6534 -13.4919 308.5 82826.087 73.5 2016 82.826087
## 67 67 52.5408 -13.2676 419.5 167196.402 142.4 2016 167.196402
## 68 68 52.6955 -12.8570 502.5 53253.373 33.0 2016 53.253373
## 69 69 52.4507 -12.0559 330.0 40848.576 57.0 2016 40.848576
## 70 70 52.6219 -12.2969 401.0 90224.888 99.0 2016 90.224888
## 71 71 52.7735 -12.3392 371.0 168533.733 214.4 2016 168.533733
## 72 72 52.6403 -12.5908 523.0 94152.924 79.5 2016 94.152924
## 73 73 52.0337 -12.0698 721.5 16461.769 9.0 2016 16.461769
## 74 74 52.2069 -12.2312 697.5 87346.327 61.5 2016 87.346327
## 75 75 52.3952 -12.4823 601.5 180749.625 128.9 2016 180.749625
## 76 76 52.4483 -12.7148 603.5 49445.277 33.0 2016 49.445277
## 77 77 52.5373 -12.9262 550.0 114080.960 88.5 2016 114.080960
## 78 78 52.3982 -13.1029 543.5 66356.822 43.5 2016 66.356822
## 79 79 52.1400 -13.2446 606.5 34422.789 24.0 2016 34.422789
## 80 80 52.0511 -13.5145 495.5 40089.955 24.0 2016 40.089955
## 81 81 51.8873 -13.5214 581.0 42218.891 30.0 2016 42.218891
## 82 82 51.7320 -13.4943 710.5 17970.015 12.0 2016 17.970015
## 83 83 51.4595 -13.6170 723.5 23478.261 13.5 2016 23.478261
## 84 84 51.2955 -13.7462 686.0 13403.298 7.5 2016 13.403298
## 85 85 51.2869 -14.0051 522.5 51094.453 33.0 2016 51.094453
## lan lat long prof peso.gr numero camp
## 1 1 51.1471 -13.9298 531.5 12053.973 9.0 P16
## 2 2 51.1011 -14.1830 628.5 3898.051 3.0 P16
## 3 3 51.1372 -14.4533 638.5 12443.778 9.0 P16
## 4 4 50.9836 -14.2370 748.0 0.000 0.0 P16
## 5 5 51.3002 -14.5562 593.5 4857.571 3.0 P16
## 6 6 51.4554 -14.7745 639.0 11844.078 7.5 P16
## 7 7 51.6273 -14.7835 572.0 44137.931 30.0 P16
## 8 8 51.7889 -14.7824 485.5 12983.508 10.5 P16
## 9 9 51.9642 -14.8531 495.5 13403.298 10.5 P16
## 10 10 52.1994 -14.6672 406.0 50104.948 34.5 P16
## 11 11 52.3914 -14.8265 611.0 0.000 0.0 P16
## 12 12 52.5173 -14.6387 412.0 49295.352 33.0 P16
## 13 13 52.4468 -14.3837 347.0 40149.925 28.5 P16
## 14 14 52.4544 -14.1025 311.5 31035.982 27.0 P16
## 15 15 52.3609 -13.8595 349.0 28302.849 25.5 P16
## 16 16 52.4373 -13.5133 361.5 44647.676 36.0 P16
## 17 17 52.2815 -14.4153 352.0 35982.009 36.0 P16
## 18 18 52.1550 -14.1128 344.0 108125.937 93.0 P16
## 19 19 52.1276 -13.8451 403.0 80539.730 49.5 P16
## 20 20 52.2124 -13.4927 436.5 97001.499 75.0 P16
## 21 21 51.8779 -14.4495 360.0 39340.330 22.5 P16
## 22 22 51.7396 -14.4448 384.0 67151.424 45.0 P16
## 23 23 51.5521 -14.5670 507.0 53583.208 36.0 P16
## 24 24 51.4685 -14.1850 446.0 61109.445 43.5 P16
## 25 25 51.6924 -13.7936 502.5 16881.559 12.0 P16
## 26 26 51.7289 -14.1079 381.0 44407.796 27.0 P16
## 27 27 51.8743 -13.9758 379.5 36581.709 21.0 P16
## 28 28 51.9700 -13.7536 442.0 34992.504 28.5 P16
## 29 29 52.8863 -12.8234 428.0 83268.366 57.0 P16
## 30 30 52.8840 -12.6118 417.0 43319.340 42.0 P16
## 31 31 52.9644 -12.3519 326.0 92563.718 175.4 P16
## 32 32 53.0122 -12.0771 233.0 112773.613 454.3 P16
## 33 33 53.5221 -11.6332 215.0 20569.715 61.5 P16
## 34 34 53.8113 -11.4031 252.0 48062.969 52.5 P16
## 35 35 53.9852 -11.3952 287.0 174497.751 121.4 P16
## 36 36 53.9398 -11.9261 370.5 75772.114 70.5 P16
## 37 37 53.8415 -13.3700 362.0 30014.993 22.5 P16
## 38 38 53.9635 -12.9041 596.0 4917.541 3.0 P16
## 39 39 53.8543 -12.5745 380.5 20059.970 22.5 P16
## 40 40 53.8930 -12.1672 375.5 78860.570 70.5 P16
## 41 41 53.4660 -12.6767 297.5 54158.921 54.0 P16
## 42 42 53.5125 -12.4703 315.0 118665.667 96.0 P16
## 43 43 53.5162 -12.2190 329.5 37952.024 39.0 P16
## 44 44 53.6031 -11.9335 296.0 74962.519 63.0 P16
## 45 45 53.3864 -12.0448 280.5 46248.876 185.9 P16
## 46 46 53.3008 -12.4400 360.5 94146.927 134.9 P16
## 47 47 53.3623 -13.0208 271.5 27496.252 43.5 P16
## 48 48 53.1860 -12.7345 353.5 117503.748 122.9 P16
## 49 49 53.0288 -12.8775 379.0 83169.415 103.4 P16
## 50 50 52.9543 -13.0919 361.5 204197.901 242.9 P16
## 51 51 53.6493 -13.6562 270.0 5922.039 9.0 P16
## 52 52 53.5385 -14.1104 330.0 25187.406 27.0 P16
## 53 53 53.3838 -14.1155 225.5 7976.012 21.0 P16
## 54 54 53.3056 -14.3913 350.5 68230.885 63.0 P16
## 55 55 53.1272 -14.3615 232.5 111754.123 97.5 P16
## 56 56 53.0369 -14.7581 663.5 2371.814 1.5 P16
## 57 57 52.7801 -14.4977 401.5 157061.469 128.9 P16
## 58 58 52.7032 -14.8204 643.5 6836.582 4.5 P16
## 59 59 52.6840 -14.1928 324.5 38530.735 27.0 P16
## 60 60 52.9205 -14.0399 205.5 11229.385 97.5 P16
## 61 61 53.0984 -13.3226 237.5 13493.253 27.0 P16
## 62 62 52.9616 -13.5822 209.5 6674.663 16.5 P16
## 63 63 52.8107 -13.7734 212.0 10344.828 33.0 P16
## 64 64 52.8229 -13.3382 288.0 105142.429 107.9 P16
## 65 65 52.5408 -13.7273 313.0 18590.705 18.0 P16
## 66 66 52.6534 -13.4919 308.5 82826.087 73.5 P16
## 67 67 52.5408 -13.2676 419.5 167196.402 142.4 P16
## 68 68 52.6955 -12.8570 502.5 53253.373 33.0 P16
## 69 69 52.4507 -12.0559 330.0 40848.576 57.0 P16
## 70 70 52.6219 -12.2969 401.0 90224.888 99.0 P16
## 71 71 52.7735 -12.3392 371.0 168533.733 214.4 P16
## 72 72 52.6403 -12.5908 523.0 94152.924 79.5 P16
## 73 73 52.0337 -12.0698 721.5 16461.769 9.0 P16
## 74 74 52.2069 -12.2312 697.5 87346.327 61.5 P16
## 75 75 52.3952 -12.4823 601.5 180749.625 128.9 P16
## 76 76 52.4483 -12.7148 603.5 49445.277 33.0 P16
## 77 77 52.5373 -12.9262 550.0 114080.960 88.5 P16
## 78 78 52.3982 -13.1029 543.5 66356.822 43.5 P16
## 79 79 52.1400 -13.2446 606.5 34422.789 24.0 P16
## 80 80 52.0511 -13.5145 495.5 40089.955 24.0 P16
## 81 81 51.8873 -13.5214 581.0 42218.891 30.0 P16
## 82 82 51.7320 -13.4943 710.5 17970.015 12.0 P16
## 83 83 51.4595 -13.6170 723.5 23478.261 13.5 P16
## 84 84 51.2955 -13.7462 686.0 13403.298 7.5 P16
## 85 85 51.2869 -14.0051 522.5 51094.453 33.0 P16p<-ggplot2::ggplot(hake)+
geom_polygon(aes(long,lat,group=group),data=Porc,fill="white",color="darkgrey")+
geom_point(aes(x=long,y=lat,size=sqrt(numero),text=lan),color="blue")+
scale_size_continuous(name="No. ind.")+coord_fixed(1.3)## Warning: Ignoring unknown aesthetics: textggplotly(p,tooltip=c("text","lance"),width=800,height=500)library(knitr)
library(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowsoptions(knitr.table.format = "markdown")
kable(databICES(1,50,"N16","Cant"),digits=2,caption="Merluza en 2016 Cantábrico y Galicia") %>%
kable_styling(bootstrap_options="condensed",full_width=F,position="center")## Warning in kable_styling(., bootstrap_options = "condensed", full_width = F, :
## Please specify format in kable. kableExtra can customize either HTML or LaTeX
## outputs. See https://haozhu233.github.io/kableExtra/ for details.| 9.aN_Avg | 9.aN_SE | 8.cW_Avg | 8.cW_SE | 8.cE_Avg | 8.cE_SE | Tot_Avg | Tot_SE | |
|---|---|---|---|---|---|---|---|---|
| Merluccius merluccius_N16_p | 12.84 | 2.35 | 9.52 | 1.40 | 4.77 | 0.47 | 7.68 | 0.65 |
| Merluccius merluccius_N16_n | 302.93 | 44.60 | 327.89 | 46.93 | 107.67 | 14.37 | 211.56 | 18.35 |
Nort<-ggplot2::map_data(Nort.map)
head(Nort)ggplot2::ggplot(data=Nort)+geom_polygon(aes(long,lat,fill=region,group=group),col="white")+
coord_fixed(1.3)